“Many companies expect data scientists to be full-stack, which includes knowing lower-level infrastructure tools such as Kubernetes (K8s) and resource management.”
Companies want one hire who does statistics, modeling and YAML. The real gap is that development and production look nothing alike. Containers and a decent abstraction layer close that gap. Making the data scientist learn Kubernetes just means paying a statistician to debug pods.